Nutvara J, Kaitlyn Ng, Kittibhum Tasanasuwan
Transportation policy-makers often face the challenge of choosing between various transportation scenarios. To do this, they need to understand the flow of origin and destination (OD) of people. King County has diverse land use, including urban areas like Seattle, small towns like Issaquah, and rural areas like Snoqualmie. With different land uses, the population demographics also vary. Therefore, understanding how demographics and land use affect travel patterns is crucial for urban planning and transportation infrastructure development.
This study aims to analyze the correlations between travel patterns, demographics, and land use. The study seeks to answer the following questions:
Our data came from three sources for King County in 2019.
Correlation coefficients are used to find how strong a relationship is between data. The formulas return a value between -1 and 1, where
In this study, we want to observe the relationship between demographics and OD flows categorized by different land uses.
In Urban Character Residential, OD flows are slightly higher correlated with the percentage of the population using transit and walking to work. This correlation suggests that more people choose transit and walking modes of transportation for their daily commute, leading to higher OD flows associated with them.
In Intensive Urban, OD flows are positively correlated with To Work by Walk, Ratio Rent to Income, and Ratio House Value to Income,and negatively correlated with Going To Work by Car. This indicates a preference for walking as a mode of transportation in these areas in contrast to driving. Also, if the cost of renting and house value relative to income is higher, there are increased OD flows.
In Industrial Areas, All Transportation modes to work have strong positive correlations with OD flows, except car that is strong negative. Unemployment also shows a noticeable negative correlation with OD flows. If percentages of alternative modes are higher, the OD flows are higher. This reflects that population a reliance on alternative modes of transportation to work. However, if unemployment is lower, the OD flows are higher.
In Other Areas, the Percentage of Black race, the Percentage Below Poverty, and Rent/Income have strong positive correlations with OD flows. Driving to work* also shows a strong negative correlation with OD flows. This indicates that if areas are populated by Black people, below poverty, or transit users, their OD flows are higher as well.
Linear regression is a statistical technique used to describe relationships among variables. It can predict the relationship between variables by assuming a linear connection between the one or several independent variables (x) and dependent variable (y). The formula is given as:
Y = $B_0$ + $B_1X_1$ + $B_2X_2$ + ... + $B_pX_p$ + $\epsilon$
Where
This study uses regression analysis to answer the following questions 1) Does race relate with trip number? 2) What type of land use has impact to the number of trip origin and trip destination? 3) Which factor effect number of the trip more: unemployment or poverty level? 4) What type of land use has higher number of transit to work use? what about WFH?
Remove rows with NaN value and land use data that is 'N/A' type or 'Undesignated' type
Inspecting land use type data distribution
To handle imbalance data, "Active Open Space and Recreation" and "Rural Character Residential" are excluded from the analysis as these data are too small. "Intensive Urban" and "Industrial" are combind and definded as "Mixed Urban" area type.
The dependent variable is the sum of original flow and destination flow. The independent variables are percentage of each race in each census
A positive coefficient indicates that as the predictor variable increases, the Target variable also increases. From the result, as percentage of one race in census increase, the number of total trip also increase. Census with most majority is Asian trend to produce trip the most
Plots below show relationship of race dominant and number of trip origin
This analysis use original flow and destination flow as dependent value. The independent variables are land use type (categorical value)
Box plot shows that 'Intensive urban resident' and 'Urban Characteristic Resident' has wide range of the number of trip, however, most of data are outliner which indicated that there are some special area where there are trip generated more than usual. From regression analysis, it can indicate that rural residential area are likely to generate amouth of trips. This make sense as facilities might be limited in rural area so people need to make trip to do their activities
This analysis use original flow and destination flow as dependent value. The independent variables are percentage of unemployment population and percentage of population with BelowPovertyLevel
From scatter plot, percentage of unemployment and population with below poverty level are related. However, these two factor has different effect to the number of trip. From linear regression analysis, area with higher unemployment makes more trip than area with people below poverty level. This can be concluded that people below poverty cannot affroad travel expense than unemployememt people
This analysis use percentage of people who use transit to work or percentage of people who work from home (WFH) as dependent value. The independent variables are land use type (categorial)
Percentage of people who WFH is lower than people who transit to work in every land use type. People mostly use transit to work in Industrial and Intencive Urban area. As these area mostly have good transit facility and mainly serving for people who working. However, people who live in intenseive urban area tend to WFH the most. This indicated that intensive urban area must have both office and residential area.
These graphs will provide insight on the relationship between OD flows, demographics, and land use.
Transit OD flows are plotted via scikit-mobility, which is a Python library for human mobility analysis. Their visualizations are built on top of folium.